WELCOME EVERYONE GOOD
AFTERNOON AND THANK YOU FOR
JOINING US.
I'M YOUR HOST FOR TODAY'S EVENT
ORGANIZED BY NATIONAL LANGUAGE
PROCESSING AND TEXT MINING
SPECIAL INTEREST GROUP AT NIH.
LEARNING HAS TRANSFORMED THE
FIELD BUT THE IMPACT EXTENDS FAR
BEYOND THE FIELD OF BIOLOGY AS
WELL.
TODAY WE'RE FORTUNATE TO HAVE
TWO EXCEPTIONAL SPEAKERS WHO
WILL DISCUSS HOW DEEP LEARNING
IS BEING APPLIED TO THE FIELD OF
COMPUTATIONAL BIOLOGY.
THIS EVENT IS GIVEN THE
RECOGNITION OF ALPHA FOLD IN THE
NOVEL PRIZES HIGHLIGHTING THE
GROUND BREAKING ROLE OF A.I. IN
SCIENTIFIC DISCOVERY.
OUR TWO SPEAKERS ARE DR. LAUREN
PORTER AND DR. IVAN OVCHARENKO.
THEY'RE BOTH TENURE TRACK
INVESTIGATORS AT THE DIVISION OF
INTRAMURAL RESEARCH.
DR. PORTER WILL KICK OFF THE
EVENT FOLLOWED BY
DR. OVCHARENKO.
IF YOU HAVE QUESTIONS FOR
DR. PORTER USE THE LIVE FEEDBACK
BUTTON AVAILABLE IN YOUR
VIDEOCAST SCREEN AND WE'LL HAVE
A QUESTION AND ANSWER SESSION
WITH HER AROUND 2:25 AND 2:30
BEFORE SWITCHING TO
DR. OVCHARENKO.
WITH THAT I'M EXCITED TO INVITE
TO THE FLOOR, DR. LAUREN PORTER.
>> THANK YOU.
I WILL SHARE MY SCREEN.
HOLD ON A SECOND.
I HAVE A PROBLEM.
>> IT LOOKS LIKE IT'S ONLY THREE
OF US, YOU, ME AND LAUREN.
DO YOU WANT TO GIVE OTHERS FIVE
MORE MINUTES TO GIVE THEM A
CHANCE TO JOIN OR I MIGHT BE
MISSING THE LIST OF
PARTICIPANTS.
>> IVAN, WE'RE IN THE CLOSED
GROUP IN THE ZOOM SESSION IT'S
ONLY US AND EVERYBODY ELSE IS
JOINING THROUGH THE VIDEOCAST.
WE HAVE MANY MORE PEOPLE JUST
WATCHING US THROUGH THE VIDEO.
>> OKAY, MY APOLOGIES THEN.
>> THEY'LL SEND QUESTIONS IN SO
I'LL BE HAPPY TO READ OUT THE
QUESTIONS AND COMMENTS FROM
THEM.
>> OKAY.
THANK YOU.
>> ALL RIGHT.
WELL, THANK YOU, LANA FOR THE
INVITATION.
YOU CAN SEE I CHANGED MY TITLE
TO TODAY WE'RE GOING TO TALK
ABOUT CLASS OF PROTEINS CALLED
FOLD SWITCHING PROTEINS THAT
SHOW SOMETHING ABOUT LIMITATIONS
OF ALPHA FOLD AS POWERFUL AS IT
IS.
IF WE HAVE TIME AT THE END WE'LL
TALK A LITTLE BIT ABOUT HOW IT'S
WORKING.
SO IF YOU LOOK AT THE REQ SEQ
DATABASE IT HAS ABOUT 3.6 NON
REDUNDANT SEQUENCES FROM MANY
OREN
ORENI -- ORGANISMS.
AN NATURAL QUESTION THAT COMES P
IS HOW DO THEY REPRESENT THE
PROTEIN UNIVERSE.
IF YOU TAKE ANYTHING FROM THE
TALK I HOPE WHAT YOU'LL TAKE IS
IT'S NOT A FULL REPRESENTATION
AND WE STILL DON'T REALLY KNOW
HOW COMPLETE OR INCOMPLETE THE
REPRESENTATION IS.
SO, WHAT DO I MEAN BY THAT?
HERE'S AN EXAMPLE OF WHAT I MEAN
OF A PROTEIN THAT KIND OF DEFIES
EXPECTATIONS.
THIS PROTEIN IS CALLED RFH.
IT HAS AN INTERNAL DOMAIN YOU
CAN SEE IN GRAY WHICH BINDS RNA
POLYMERASE AND C TERMINAL DOMAIN
IN TEAL WHICH IS IMPORTANT FOR
AUTO INHIBITION.
THIS PROTEIN ONLY WORKS AT SITES
WITHIN AN OPS DNA SEQUENCE.
BUT UPON BINDING THE SEQUENCE
AND POLYMERASE THE C TERMINAL
DOMAIN DE TAXES FROM THE END
TERMINAL DOMAIN AND REFOLDS TO A
BETA BARREL CONFIRMATION THAT
BINDS THE INTEGRAL SUB UNIT OF
THE RIBOSOME FOSTERING EFFICIENT
TRANSLATION.
WHEN I FIRST LEARNED PROTEINS
COULD DO THIS, I THOUGHT WOW.
I DIDN'T THINK PROTEINS COULD
DRAMATICALLY AND REVERSIBLY
SWITCH THEIR PROTEIN BUT IT'S
IMPORTANT FOR REGULATORY
MECHANISMS AND A NUMBER OF HUMAN
DISEASES.
HERE'S A FEW EXAMPLES.
TUBERCULOSIS, ALZHEIMER'S,
GASTROENTERITIS AND THE PROBLEM
WITH SWITCHING PROTEINS THERE'S
SO LITTLE WE KNOW.
IT'S A VERY DATA POOR AREA RIGHT
NOW.
SO THERE ARE A LOT OF BARRIERS
TO UNDERSTANDING HOW THE
PROTEINS ARE WORKING AND WHAT
THEY'RE ROLES ARE IN BIOLOGY.
SO, I BELIEVE IF WE CAN
UNDERSTAND HOW IT CHANGES IN OUR
ENVIRONMENT CAN CAUSE PROTEINS
TO CHANGE THEIR FOLDS AND
FUNCTION COULD HAVE SIGNIFICANT
IMPLICATIONS.
IT COULD GIVE US A MORE
UNDERSTANDING OF PROTEIN FOLDING
AND DYNAMICS AND DISCOVER NEW
BIOLOGICAL PATHWAYS SINCE WE
KNOW FOLD SWITCHING CAN LEAD TO
ALTERNATIVE FOLDED STATES.
FOR EXAMPLE THERE'S ONE MUTATION
HIGHLY ASSOCIATED WITH CANCER IN
THE MEF2B PROTEIN A D83V
MUTATION THAT IS BELIEVED TO
SWITCH THE PROTEIN SECONDARY
STRUCTURE ELEMENT FROM ALPHA
HELIX TO BETA SHEET AND
INACTIVATE THE PROTEIN AND ALSO
SPLICING AND POLYMORPHISMS AND
POST TRANSLATIONAL MODIFICATIONS
CAN COULD PROTEINS TO SWITCH
THEIR FOLDS AND FUNCTIONS.
I GAVE AN EXAMPLE OF THE SNIP
AND THERE'S PRO -- PROTEINS THAT
SWITCH FOLD.
THERE'S BARRIERS TO PROGRESS.
THERE'S NOT VERY MANY EXAMPLES
ANDS YOU KNOW IT BE HARD TO
GENERALIZE IN A DATA POOR AREA
OF RESEARCH.
FOLD SWITCHING IS IS HARD TO
PREDICT AND WHY WE'LL FOCUS OUR
ATTENTION AND THEY'RE DIFFICULT
TO HANDLE EXPERIMENTALLY.
HERE'S A PICTURE OF THREE NMR
TUBES AND THIS PROTEIN CRASHED
THE SOLUTION WITHIN MINUTES OF
ME PUTTING IT IN THE TUBES AND
IT MAY BE THIS IS A DATA POOR
AREA BECAUSE THEY'RE UNSTABLE
AND DIFFICULT TO WORK WITH.
SO TODAY WE'RE GOING TO FOCUS
ALL OF OUR ATTENTION EVEN THOUGH
MY LAB HAS A BUNCH OF
INTERESTING WE'LL FOCUS OUR
ATTENTION ON PREDICTING FOLD
SWITCHERS FROM SEQUENCE.
THE NATURAL QUESTION EVERYBODY
ASKS ME IS WHY DON'T YOU JUST
USE ALPHA FOLD AND IT TURNS OUT
FOLD SWITCHING IS EVEN DIFFICULT
FOR ALPHA FOLD.
SO BACK WHEN ALPHA FOLD WAS
FIRST PUBLISHED WE ASKED HOW IT
DOES PREDICTING THE TWO
CONFIRMATION OF FOLD PREDICTING
PROTEINS FOLD 1 AND 2 AND
MEASURED ACCURACY BY A SCORE AND
CAN GET A SIMILAR RESULT USING
RMSD.
THIS IS THE IDENTIFY LINE AND
WOULD EXPECT IF ALPHA FOLD 2
PREDICTED CONFIRMATIONS
CONSISTENTLY AND RELIABLY WE'D
SEE AN EQUAL DISTRIBUTION OF
POINTS ABOVE AND BELOW THE
DIAGONAL BUT THAT'S NOT WHAT WE
SEE.
ALPHA FOLD IS BIAS TO PREDICTING
ONE CONFIRMATION AND MISSING THE
OTHER COMPLETELY.
THIS IS AN ALPHA FOLD WHEN IT
WAS FIRST RELEASED.
YOU COULD ASK MORE THINGS COULD
BE DONE AND THE MODEL CAME OUT
AND ENHANCED SAMPLING METHODS
CAME OUT.
WHY NOT TRY THOSE.
SO WE DID.
WE ENDED UP GENERATING NEARLY
300,000 STRUCTURES RUNNING EVERY
VERSION OF ALPHA FOLD AND EVERY
ITERATION YOU CAN IMAGINATION.
WE RAN ALL PROTEIN BINDING
PARTNERS AND ON MONOMERIC
SPECIES AND EVERY SET OF
CONDITIONS WE COULD PHOR THE
STRUCTURES AND 70% OF ALL
INTERACTIONS AND THE METHODS
COMBINED AND GENERATING NEARLY
300,000 DIFFERENT STRUCTURES HAD
A PRETTY POOR SUCCESS RATE.
SO WE'RE ONLY GENERATING OUT OF
92 FOLD SWITCHING PROTEINS ONLY
GETTING ABOUT 35% SUCCESS RATE
AND THESE ARE FOR PROTEINS THAT
WERE PROBABLY IN ALPHA FOLDS
TRAINING SET BECAUSE THEY WERE
PUBLISHED MAY OF 2018.
SO I WOULD SAY IT SHOWS ALPHA
FOLD IS A WEAK PREDICTOR OF
SWITCHING PROTEINS.
CAN ALPHA FOLD DO BETTER?
WE NEED TO KNOW HOW IT WORKS.
WE TAKE AN INPUT SEQUENCE AND
USE A GENETIC DATABASE TO MAKE A
MULTIPLE SEQUENCE ALIGNMENT AND
ALPHA FOLD TAKES THIS AND MAKES
AN MSA REPRESENTATION AND USES
THE IMPORTANT BLOCK CALLED THE
EVO FORMER A CNN TO GENERATE A
PAIR REPRESENTATION FROM WHICH A
STRUCTURE CAN BE MADE.
SO BASICALLY THIS PAIR
REPRESENTATION PROVIDES
RESTRAINTS THAT ALLOWS ALPHA
FOLD TO MAKE A STRUCTURE AND
SOMETIMES PASSING IT BACK TO THE
MODEL AND RERUNNING THE MODEL IN
A PROCESS CALLED RECYCLES MAKES
FOR BETTER PREDICTIONS.
THE QUESTION IS HOW DOES ALPHA
FOLD PREDICT FOLD SWITCHED
CONFIRMATION?
IT CAN CONFIRM ONE SWITCH BUT
WHAT ABOUT THE OTHER
CONFIRMATION?
THAT'S THE CHALLENGE.
THERE'S TWO OPTIONS HERE.
ONE IS THAT IT'S GENERATIVE
WHICH WOULD MEAN IT'S USING
ALTERNATIVE CO-EVOLUTIONARY
SIGNALS TO MAKE STRUCTURES IT
HAS NOT SEEN IN THE TRAIN SET
BEFORE AND THE OTHER OPTION
WOULD BE IT'S ASSOCIATIVE THAT
MEANS FOR PREDICTING ALTERNATIVE
CONFIRMATIONS ALPHA FOLD IS
ACTUALLY NOT USING
CO-EVOLUTIONARY INFORMATION AND
TRAINING ON ITS TRAINING FROM
THE PBB PROBABLY ASSOCIATIONS
SEQUENCES WITH STRUCTURES DURING
TRAINING.
I'M CONVINCED AND WHEN IT COMES
TO ALTERNATIVE CONFIRMATIONS OF
SWITCH PROTEINS ALPHA FOLD IS
ASSOCIATED.
HOW DO I KNOW THIS?
AS YOU KNOW EXPERIMENTS LAG
PREDICTION.
SO, RECENTLY A NUMBER OF
STRUCTURES HAVE BEEN PUBLISHED.
THE ALPHA FOLD HAS NOT PREDICTED
ACCURATELY.
LOUIS K'S GROUP HAD A STRUCTURE
OF PROINTERLEUKIN 18 WHICH DOES
NOT MATCH WHAT ALPHA FOLD
PREDICTED.
BCC ALPHA A SPLICE OF HUMAN
CANCER PROTEIN WAS RECENTLY
SOLVED.
ITS STRUCTURE WAS UNLIKE
ANYTHING IN THE PBB AND A LAB
PUBLISHED THE STRUCTURE OF THE
MP20 THOUGH ALPHA FOLD 2 AND 3
PREDICT THE FOLDS OF EACH UNIT
CORRECTLY IT GETS THE
ORIENTATION WRONG PREDICTING AN
OCTOMER OPPOSED TO TWO TETROMERS
AND AN ANCESTOR OF RIBOSOME
BINDING PROTEINS WAS ALSO SOLVED
BY CRYSTALLOGRAPHY AND PREDICTED
INCORRECTLY BY ALPHA FOLD 2 AND
3.
WHY IS THIS HAPPENING?
WHILE IT'S PREDICTING STRUCTURES
LIKELY IN IT'S TRAINING SET
RATHER THAN EXPERIMENTALLY
CONSISTENT ONES.
THERE WAS NO STRUCTURE LIKE BCC
ALPHA IN THE PDB BEFORE IT WAS
SOLVED BEFORE IT WAS TRAINED.
WHAT DID ALPHA FOLD?
IT TOOK THE SEQUENCE AND
PREDICTED SOMETHING ALREADY IN
THE TRAINING SET.
SIMILAR STORY WITH PROINT
INTERLEUKIN 18 RATHER THAN WHAT
IS DETERMINED BY EXPERIMENT IN
THE CASES.
SO WHY IS IT FAILING TO PREDICT
THE NEW FOLDS?
WELL, WHAT RECENTLY PATRICK
BRYANT AND FRANK DID MY DREAM
EXPERIMENT WHERE THEY RETRAINED
THE ALPHA FOLD ARCHITECTURE ON
ONE CONFIRMATION OF PROTEINS BUT
NOT THE OTHER AND THEY DIDN'T
MEAN TO DO THIS BUT IT TURNS OUT
THEY'RE TRAINING SET HAD THESE
DOMINANT FOLDS YOU CAN SEE IN
THE COLUMN HERE BUT THEY ONLY
HAVE ONE OF THESE ALTERNATIVE
FOLDS YOU CAN SEE HERE BUT NONE
OF THESE OTHERS.
AND WHEN YOU RUN C FOLDS, THEIR
VERSION OF ALPHA FOLD 2 ON OUR
FOLD SWITCHING PROTEINS, YOU CAN
SEE EVERYTHING IN ITS TRAINING
SET IS PREDICTED ACCURATELY AND
WITH HIGH CONFIDENCE BUT IT
COMPLETELY MISSES THESE
ALTERNATIVE CONFIRMATIONS.
AND WHAT'S REALLY INTERESTING IS
WITH THE RFH PROTEIN FOR ALPHA
FOLD 2 IT PREDICTS THE CONF
CONFIRMA
CONFIRMATION.
IF YOU GIVE C FOLD THE SAME IT
PREDICT THE BETA SEQ INFORMATION
AND THE INFORMATION IN THE FULL
MSA MATCHES BETA SHEET NOT ALPHA
HELIX.
WE THINK IS THIS CASE IT'S
MEMORIZED THE SEQUENCE AND NOT
USING CO-EVOLUTIONARY
INFORMATION TO PREDICT THIS.
WE CAN DO FANCY SEQUENCE
SAMPLING AND GET BETTER
PREDICTIONS OF SOME OF THE
PROTEINS BUT NONE OF THEM ARE
VERY ACCURATE.
ALL ARE LOW CONFIDENCE AND ALPHA
FOLD WHEN ANYTHING IS HIGH
CONFIDENCE, ALPHA FOLD ALSO
PREDICTS INCORRECT CONFIRMATIONS
THAT DON'T MATCH EXPERIMENTALLY
DETERMINED STRUCTURE WITH HIGHER
CONFIDENCE IN THE ALTERNATIVE.
THERE'S A LOT OF GROWTH TO BE
DONE IN THE AREA BECAUSE IT
DOESN'T SEEM LIKE ALPHA FOLD'S
ARCHITECTURE AS IT IS, IS WELL
SUITED TO PREDICT THE ALT
ALTERNATIVE CONFIRMATIONS
OUTSIDE ITS TRAINING SET.
IT'S MEMORIZING SOME STRUCTURES
AND MAYBE WE CAN GET IT ALPHA
FOLD TO REVEAL IT'S MEMORIZED
STRUCTURES THROUGH MINIMAL
SEQUENCE INFORMATION.
WE'RE BLINDFOLDING IT AND SAY
WE'RE NOT GOING TO GIVE YOU
ENOUGH INFORMATION WITH ROBUST
INFORMATION BUT JOG YOUR MEMORY
AND REMEMBER YOUR TRAINING.
WE HAVE OUR INPUT SEQUENCE AND
FULL LENGTH MSA AND WE CAN
GENERATE A RANDOM MSA AND SUB
SAMPLE AT VERY SHALLOW DEPTHS.
THREE AND SIX SEQUENCES.
NOT A LOT.
WE CAN CHOOSE USING AN UNBIASSED
METHOD OUR OPTIMAL SAMPLING
DEPTH AND CAPTURE THE
CONFIRMATIONS.
WE CAN CONTRAST THIS WITH A
METHOD THAT WAS RECENTLY
PUBLISHED IN NATURE THAT CLAIMED
ALPHA FOLD PREDICTIONS WERE
GENERATIVE AND THE GROUP CLAIMED
IF YOU PUT IN SHALLOW SEQUENCE
ALIGNMENTS FROM DIFFERENT
SEQUENCE BASE, ALPHA FOLD WOULD
PREDICT ALTERNATIVE INFORMATION
AND THE CO-EVOLUTIONARY
INFORMATION WAS SUFFICIENT TO
PREDICT THESE ALTERNATIVE FOLDS.
SO, IF THIS CLAIM IS CORRECT AND
ALPHA FOLD IS INDEED GENERATIVE
WE WOULD EXPECT THEIR METHOD FOR
GETTING THIS CO-EVOLUTIONARY
INFORMATION WOULD OUTPERFORM OUR
METHOD FOR RANDOM SEQUENCE
SAMPLING WHERE THEY'RE MAKING NO
PRETENSES OF CO-EVOLUTIONARY
INFERENCE.
IN FACT THE OPPOSITE IS TRUE.
CF RANDOM OUT FORMS THE CLUSTER
IN ALL THE EXPERIMENTALLY
DETERMINED TARGETS.
IT'S ALWAYS MORE ACCURATE AND
HIGHER CONFIDENCE AND IN
PERFORMANCE METRICS IT'S MORE
ACCURATE BY MATTHEW'S
CORRELATION COEFFICIENT,
COEFFICIENT AND MORE EFFICIENT
IN THE SAMPLING OF CONFIRMATIONS
AND THE NUMBER OF PREDICTIONS IS
LOWER THAN WHAT THE CLUSTER
NEEDS.
FURTHERMORE, WE CAN SHOW THAT
ACTUALLY THESE CO-EVOLUTION IS
NOT DRIVING THE PREDICTIONS OF
ALTERNATIVE CONFIRMATIONS
BECAUSE WE CAN TAKE EVERY
MULTIPLE SEQUENCE ALIGNMENT USED
BY ALPHA FOLD AND DO
CO-EVOLUTIONARY ANALYSIS ON THEM
AND WHAT YOU CAN SEE IS IN THESE
RED BOX ARE THE CASES WHERE
ALPHA FOLD IS PREDICTING
ALTERNATIVE CONFIRMATIONS.
THERE'S LESS INFORMATION ABOUT
THE ALTERNATIVE CONFIRMATION IN
ALL THESE CASES COMPARED TO THE
DOMINANT CONFIRMATION AND YET
ALPHA FOLD IS PREDICTING THE
ALTERNATIVE CONFIRMATION AND
THERE'S ESSENTIALLY NO
INFORMATION ABOUT THE
ALTERNATIVE CONFIRMATION IN ANY
OF THE SEQUENCE ALIGNMENTS WE'RE
TESTING.
AGAIN, CO-EVOLUTION IS NOT
DRIVING THESE PREDICTIONS AND WE
THINK A MORE LIKELY EXPLANATION
IS MEMORIZATION.
SO NOW I'VE SHOWN YOU THREE
CASES BUT DOES THIS METHOD
GENERALIZE?
IT TURNS OUT YES IT DOES.
IF WE RUN IT ON ALL FOLD
SWITCHING PROTEINS WE HAVE STILL
A MODEST 34% SUCCESS RATE BUT
ONLY WITH 20,000 PREDICTIONS.
YOU CAN COMPARE THIS WITH ALL
OTHER METHODS COMBINED IT GIVES
LIKE A 26%, 27% SUCCESS RATE BUT
IT'S SAMPLING 10 TIMES MORE.
I THINK CF RANDOM IS A BETTER
EXPLANATION THAN THE OTHER
METHODS PROPOSED PREVIOUSLY.
CF RANDOM RUNS NOT ONLY ON FOLD
SWITCHING PROTEINS AND OTHER
KINDS AND BINDING PROTEIN AND WE
BENCHMARKED IT AGAINST SPEECH AF
ANOTHER METHOD TO PREDICT
ALTERNATIVE CONFIRMATIONS BY TM
SCORE THEIR PERFORMANCES ARE
COMPARABLE BUT CF RANDOM TAKES
WAY LESS SAMPLING.
THIS IS THE TOTAL NUMBER OF
STRUCTURES USED TO GENERATE THE
ENSEMBLES WHEREAS SPEECH AF IS
THE NUMBER OF GOOD STRUCTURES
THEY GENERATED NOT THE TOTAL
NUMBER.
SO WE'RE SAMPLING THREE TIMES
LESS, THREE TIMES, FIVE TIMES
MAYBE SIX OR SEVEN TIMES LESS
THAN SPEECH AF AND PERFORMING
THE SAME.
FURTHERMORE, WE CAN COMPARE THIS
TO ANOTHER METHOD CALLED AF
SAMPLE 2 TESTED ON ANOTHER DATA
SET CALLED THE OC23.
YOU CAN SEE IT HAS A HIGH
SUCCESS RATE ONCE AGAIN AND YET
IT SAMPLES WAY WAY LESS AS
OPPOSED TO 1,000 STRUCTURES AND
HITTING THE RIGHT ANSWER PRETTY
MUCH EVERY TIME.
SO WE THINK THIS DATA ALL
TOGETHER IS A GOOD EXPLANATION
NOTHING TO DO WITH
CO-EVOLUTIONARY INFERENCE BUT
RATHER HAVING TO DO WITH MEM
RISING FROM THE TRAINING SET OR
IN SOME CASES GIVING A SMALL
AMOUNT OF CO-EVOLUTIONARY
INFORMATION THAT ALLOWS MORE IN
A GIVEN CONFIRMATION SO, IT'S
NOT SWITCHING FOLDS BUT FEWER
CONSTRAINTS ALLOW MORE MOVEMENT.
HOPEFULLY AT THIS POINT YOU'RE
CONVINCED ALPHA FOLD IS
ASSOCIATIVE.
THEN THE QUESTION BECOMES IS HOW
IS IT MAKING THESE ASSOCIATIONS?
WE'RE ABLE TO ANSWER BY LOOKING
AT BEAUTIFUL WORK BY BRIAN
WILTMAN'S GROUP LOOKING AT XCL1
WIN A MONOMERIC CONFIRMATION AND
A DIFFERENT HYDROGEN BOND
NETWORK AND HYDROPHOBIC PACKING
YOU CAN SEE HERE.
IF YOU PUT SEQUENCE INTO ALPHA
FOLD 3 AND ASK IT TO PREDICT A
DIMER IT GIVES THIS CRAZY THING
NOTHING LIKE THE EXPERIMENT.
HOWEVER, IF YOU PUT IN THAT SAME
SEQUENCE TO ALPHA FOLD WITH A
SINGLE SEQUENCE AND ZERO
RECYCLES WHICH IS THE BEST PROXY
FOR MEMORIZE BECAUSE IT'S NOT
USING CO-EVOLUTION AND ONLY
GOING THROUGH THE NETWORK ONCE
YOU CAN SEE IT DOES WELL GETTING
THE ALTERNATIVE CONFIRMATION.
WE THINK ALPHA FOLD MEMORIZED
THE STRUCTURE DURING TRAINING.
WHAT'S COOL ABOUT BEING ABLE TO
USE THIS SINGLE SEQUENCE MODEL
IS THEN WE CAN START MODELLING
MUTATIONS AND IT TURNS OUT THAT
BRIAN'S GROUP MADE A BUNCH OF
THEM.
HERE THEY ARE.
NOT ONLY A BUNCH OF MUTANTS BUT
TESTED WHICH ONES FOLD AND WE
COULD RUN ALPHA FOLD ON EVERY
ONE OF THESE MUTANTS AND SEE HOW
IT DOES.
SO HERE ARE THE RESULTS.
SO IF IT'S ONLY PREDICTING THE
MONOMERIC FOLD AND NOT FOLD
SWITCHING WE'D EXPECT TO SEE
ONLY THESE RED PLOTS ABOVE THE
DOTTED LINE.
SO YOU CAN SEE IN THE FOUR
CASES, THAT'S EXACTLY WHAT WE'RE
SEEING AND THESE ARE CORRECT
PREDICTIONS.
UNFORTUNATELY ALL THE RED BOXES
ARE CASES WHERE IT'S
MISPREDICTING THESE PROTEINS
SWITCH FOLDS WHEN ACTUALLY THEY
DON'T.
SO WHAT'S GOING ON?
WHY IS IT MAKING THE MISS
PREDICTIONS?
TWO TALENTED STUDENTS IN MY LAB
WENT AND GOT ALL THE PDB
STRUCTURES FOR BOTH FOLDS IN
ALPHA FOLD'S TRAINING SET AND
COMPARED THEIR SEQUENCES.
AND THEY FOUND WHEN YOU MAKE
THESE COMPARISONS THERE'S THREE
POSITIONS THAT DIFFER FROM ONE
ANOTHER, POSITION 11, 38 AND 33.
-- 43, SORRY.
AND WHEN YOU HAVE THE MUTATIONS
UNIQUE TO THE ALTERNATE FOLD YOU
GET A FOLD SWITCH PREDICTION AND
IF NOT YOU GET A CHEMOKINE
PREDICTION.
SO THEY INVESTIGATED EVERY
INDIVIDUAL MUTATION AND SAID
OKAY, POSITION 11 IS IMPORTANT
FOR PREDICTING THE FOLD.
BUT OUT TURNS OUT REGARDLESS OF
WHICH AMINO ACID THEY HAVE IN
POSITION 11, ALPHA FOLD IS
PREDICTING THE SAME STRUCTURES.
FURTHERMORE, THIS IS THE SAME
THING TRUE FOR I38 FOR POSITION
38 WHETHER A T OR I
CORRESPONDING TO ONE FOLD OR
ANOTHER PREDICTING THE SAME
THING IN BOTH CASES.
HOWEVER, IF WE LOOK AT POSITION
43, WE GET A VERY DIFFERENT
RESULT.
SO JUST SWITCHING THIS ONE
MUTATION FROM AN L TO AN R
COMPLETELY ALMOST COMPLETELY
CHANGES THE FOLD TO THE
CHEMOKINE FOLD WHEREAS IF WE DO
AN L 43L IT CHANGES TO THE
DIMERIC FOLD.
WHAT'S INTERESTING ABOUT THIS IS
ALPHA FOLD THIS MUTATION HAS
NOTHING TO DO WITH STRUCTURE.
IT'S FUNCTION.
THIS IS A CONSERVED SALT BINDING
SITE IN THE CHEMOKINE FOLD BUT
BECAUSE ALPHA FOLD IS
ASSOCIATING THE HIGH FUNCTIONAL
CONSERVATION AS A STRUCTURAL
DETERMINATE.
THIS IS NOT BASED ON BIO PHYSICS
BUT RATHER AMINO ACID PATTERNS
ALPHA FOLD HAS SEEN DURING
TRAINING.
SO GOING TO PREDICTING SWITCHING
FROM SEQUENCE IT STRUGGLED
WITWITH
ALTERNATIVE CONFIRMATIONS AND
THEY'RE ASSOCIATIVE MEANING
PREDICTS OF ALTERNATIVE
CONFIRMATIONS ARE LIMITED BY ITS
TRAINING SET AND SEQUENCE
ALIGNMENTS HAVE ALTERNATIVE
CONFIRMATIONS.
ALPHA FOLD'S MISSING IT RIGHT
NOW AND HOPEFULLY THEY CAN
DEVELOP A BETTER METHOD TO PICK
IT UP.
SO WITH THAT I WOULD LIKE TO
THANK THE MEMBERS OF MY GROUP
AND VARIOUS CONTRIBUTORS TO THE
WORK AND I'D ESPECIALLY LIKE TO
THANK DEVLINA AND JOE AND LESLIE
WHO DID THE BULK OF THE WORK AND
HAPPY TO TAKE QUESTIONS.
>> THIS WAS SO INTERESTING AND
EXCITING.
YOU HAVE GIVEN US SO MUCH FEED
FOR THOUGHT.
WHY IS ALPHA FOLD NOT FORMING
WELL?
IS THERE NOT ENOUGH DATA FOR THE
TRAINING?
WHY DO YOU THINK IT'S FAILING?
>> AS I SAID, IT'S A DATA POOR
AREA.
FOLD SWITCHING IS DATA POOR.
WHAT MAKES ALPHA FOLD IS THE
LARGE TRAINING SET IN THE PDB.
BECAUSE THERE'S SO FEW EXAMPLES
OF FOLD SWITCHING, ALPHA FOLD
DOES NOT HAVE ENOUGH DATA TO
GENERALIZE.
I ALSO THINK THERE'S ISSUES WITH
HOW IT WAS TRAINED.
IT KIND OF IMPLICITLY ASSUMES
THE WAY IT'S TRAINED AND USED
THAT A SINGLE AMINO ACID
SEQUENCE ASSUMES ONE STRUCTURE.
THAT MAKES IT HARD FOR IT TO BE
ABLE TO PREDICT TWO STRUCTURES
IF AN IMPLICIT ASSUMPTION IS
GOING TO ONE FOLD.
ALSO THE WAY IT'S TRAINED IT'S
TRAINED ON DEEP MULTIPLE
SEQUENCE ALIGNMENTS.
ONE THING TO IMPROVE IT IS NOT
ONLY TRAIN IT ON THE DOMINANT
FOLD AND SEQUENCE AND SHALLOW
SUB FAMILY ALIGNMENTS MY LAB
HAVE SHOWN CONTAIN INFORMATION
ABOUT THE ALTERNATIVE FOLD AND
USE THAT TO ASSOCIATE WITH THE
ALTERNATIVE FOLD.
SO THAT MAY BE A WAY TO IMPROVE
ITS ACCURACY.
>> SOUNDS LIKE AT SOME FINE
TUNING IS NEEDED ON TOP OF THE
MODEL LEARNING.
>> YES.
>> WE HAVE A QUESTION FROM THE
AUDIENCE.
ARE CHAPERONE PROTEINS INVOLVED
IN THE WHOLE SWITCHING PROTEINS?
>> THAT'S A GREAT QUESTION.
AS FAR AS I KNOW THE ANSWER IS
NO.
CERTAINLY THE EXAMPLES I
FOCUSSED ON TODAY DO NOT REQUIRE
SHAM ROANS.
-- CHAPERONES.
>> THANK YOU VERY MUCH FOR
REPLYING TOE THAT QUESTION.
-- TO THAT QUESTION.
I DON'T SEE ANYMORE QUESTIONS
FROM THE AUDIENCE.
THANK YOU, LAUREN.
THIS WAS INTERESTING.
DOES ANYBODY ELSE HAVE QUESTIONS
BEFORE WE SWITCH TO
DR. OVCHARENKO?
>> I HAVE TO PICK UP MY KIDS
SOON SO THIS IS YOUR CHANCE.
>> IF SOMEBODY HAS TO ASK MORE
QUESTIONS, KEEP SENDING THEM AND
HOPEFULLY I CAN FORWARD THEM TO
DR. PORTER.
I KNOW IT TAKES TIME.
THE TALK IS SO RICH IN
INFORMATION IT TAKES TIME TO
COME UP WITH QUESTIONS SO IF WE
GET MORE QUESTIONS I'LL FORWARD
THEM TO LAUREN.
THANK YOU SO MUCH FOR YOUR TIME.
THIS IS EXTREMELY INTERESTING.
>> THANK YOU.
>> ALL RIGHT.
SO NOW WE'RE SWITCHING GEARS AND
READY FOR DR. OVCHARENKO.
>> THANK YOU SO MUCH, LANA.
CAN I SEE MY SCREEN?
>> WE CAN.
>> WONDERFUL.
SO AFTER THIS FASCINATING TALK
BY LAUREN AND PROTEIN FOLDS I
WOULD LIKE TO SHIFT THE DEARS
COMPLETELY AND MOVE AWAY FROM
PROTEINS AND DIVE INTO THE DNA
OF THE HUMAN GENOME AND TELL YOU
ABOUT THE WORK OF MY LAB IN
TRYING TO FIGURE OUT HOW WE CAN
IDENTIFY REGULATORY VARIANTS
USING DEEP LEARNING.
OVER 20 YEARS AGO THE HUMAN
GENOME HAS BEEN SEQUENCED WITH
30,000 GENES AND WE CAN LOOK
SPECIFICALLY AT THE DNA THAT'S
NOT GENEIC.
AND THAT COMPRISES 98% OF THE
SEQUENCE OF THE HUMAN GENOME.
SINCE THE SEQUENCING OF THE
HUMAN GENOME THERE HAVE BEEN A
LOT OF SPECULATIONS THAT THESE
ARE NOT PART OF THE GENOME COULD
BE JUNK.
YOU HAVE DNA, PSEUDO GENES,
MICROSATELLITES, ETCETERA.
AND 20 YEARS AGO MY GOOD FRIEND
WHO TEACHES AT THE UNIVERSITY OF
CHICAGO DECIDED TO DO A CRAZY
EXPERIMENT.
HE SAID WHY DON'T WE LOOK AT THE
GENOME AND IDENTIFY THOSE MANY
GENES NOT THE DNA AND CALL THEM
GENE DESERTS AND TAKE THEM OUT
COMPLETELY OF THE MOUSE GENOME
AND DID THAT WITH TWO OF THE
GENES AND CAME UP WITH AN
ABSOLUTELY NORMAL VIABLE AND
HEALTHY MICE SUGGESTING INDEED
HUGE CHUNKS OF MOUSE WITH
GENOMES ARE COMPLETELY
DEFENDABLE AND
[AUDIO DIGITIZING]
AND LET'S LOOK AT THE DIFFERENT
EXAMPLE AT LACTOSE INTOLERANCE.
SO LACTOSE INTOLERANCE IS AN
ACQUIRED TRAIT OF THE HUMAN
POPULATION.
LACTOSE TOLERANCE TRACES BACK
INTO THE DOMESTICATION OF COWS
AND MAINLY PRESENT IN THE
EUROPEAN PART AND SOME PARTS OF
AFRICA AND ASIA WHERE THE
DOMESTICATION OF COWS TOOK
PLACE.
WE KNOW LACTOSE TOLERANCE AND
INTOLERANCE, DEPENDING ON HOW
YOU LOOK AT IT IS ASSOCIATED
WITH A SINGLE NUCLEOTIDE
MUTATION CLOSE TO THE LACTASE
GENE AND HOW IS IT TINY CHANGES
COULD HAVE PRONOUNCED IMPACT
WHEN THEY'RE IN PARTS OF NON
CODING PARTS OF THE GENOME.
WE HAVE LEARNED RECENTLY THE
MUTATION NOT ASSOCIATED WITH
JUST LACTOSE INTOLERANCE,
THEY'VE HAVE BEEN LINKED TO
DOZENS AND DOZENS OF HUMAN
DISEASES AND DISORDERS INCLUDING
WHERE THERE'S A MUTATION OF A
GAIN ALMOST IS PART OF ANOTHER
GENE LEADS TO THESE DISEASES
WITH QUITE DRASTIC PHENOTYPE.
SINGLE NUCLEOTIDE CHANGES IN NON
CODING DNA HAVE BEEN ASSOCIATED
WITH PARKINSON'S, HEART
DISEASES, TYPE II DIABETES AND A
VARIETY OF CANCERS.
ETCETERA.
AND IT COULD BE THAT PARTS OF
THE NON CODING GENOME ARE
DISPENSABLE YET THERE'S FRAGILE
IN THE CODING PARTS OF OUR
GENOME THAT ARE UNDISPENSABLE
AND LOOKING AT STUDIES YOU'LL
SEE THAT ONLY 5% OF THESE
VARIANTS ARE COORDINATED AND 95%
OF THEM ARE NON-CODING.
SUGGESTING THERE'S QUITE A FEW
OF VARIANTS ASSOCIATED WITH THE
DISEASES.
ONE IS A MUTATION IN A DISTAL
ENHANCER OF GENE LINKED TO
PARKINSON'S AND IT DISRUPTS THE
COMPLEX TO THE DISTAL ENHANCER
AND RESULTS IN THE HIGHER LEVEL
OF THE NUCLEIN EXPRESSER.
AND I'D LIKE TO TELL YOU NOT
ABOUT THE NON-CODING VARIANTS
BUT THE COMPUTATIONAL APPROACH
WE USED TO PREDICT THE PART OF
THE NON-CODING VARIANTS ON HUMAN
GENOTYPES.
IN PARTICULAR INTERESTED IN
FIGURING WHAT IS THE CAUSAL
VARIANTS IN THE WHOLE POOL OF
ASSOCIATED GWAS VARIANTS.
WE USED DEEP LEARNING AND
REGULATORY GENOMICS THAT
ENCOMPASSES OTHER DATA SETS AS
WELL AS WE RELY ON EXPERIMENTAL
VALIDATION DONE BY OTHER
INSTITUTES.
SO FAR WE HAVE BEEN ABLE TO
DEMONSTRATE THAT THIS METHOD CAN
PREDICT NON CODING VARIANTS THAT
ARE LINKED TO AUTISM, TYPE II
DIABETES AND LYMPHOMA AND I'LL
TOUCH A LITTLE BIT ON TYPE II
DIABETES.
HOW DOES THIS METHOD LOOK LIKE?
I'M SURE THE GROUP IS VERY
FAMILIAR WITH THE DEEP LEARNING
METHODS AND THIS IS THE NEURAL
NETWORK METHOD.
WE USE TWO PHASES.
FIRST PHASE WE TAKE THE SEQUENCE
OF THE HUMAN GENOME REVIEW THE
MODELS INTO MOTIVES AND TRY TO
PREDICT THE SIGNALS IN THE HUMAN
GENOME AS RECORDED BY THOUSANDS
OF EXPERIMENTS.
WE TRY TO PREDICT WHERE
TRANSCRIPTION FACTORS ARE
BINDING AND CELL LINES AND WHAT
IS THE CHROMATIN STATE.
THEN WE TAKE THAT INFORMATION
AND VIEW THE SECOND MODEL TRICK
TO PREDICT REGULATORY ELEMENTS.
THOSE ARE FUNCTIONAL BLOCKS IN
THE NON-CODING THAT CONTROL GENE
EXPRESSION AND REGULATE HOW AND
WHEN DIFFERENT GENES GET
EXPRESSED.
WE CALL IT A TWO-PHASE ENHANCER
MODEL AND NAME IT.
HOW DOES IT LOOK WHEN WE APPLY
THE MODEL?
WE GET VALUES THAT ARE CLOSE TO
90%, 95% FOR REGIONS OF OPEN
CHROMATIN AND HISTONE MARKS AND
TRANSCRIPTION FACTORS AND THAT'S
FINE.
WE GET OVER ALL ON THE CURVE OF
AROUND 85% AND THAT WAS THREE
YEARS AGO AND NOW WE GET 95% AND
IT DOESN'T MATTER WHAT THE
NUMBER IS.
DEEP LEARNING IS SO MUCH BETTER
AND CLUSTERS AROUND 77% AND GOOD
FOR OUR GROUP BECAUSE WE HAVE
BEEN FIGHTING THE INACCURACY OF
PREDICTIONS FOR DECADES AND WHEN
WE GOT OUR HANDS ON DEEP
LEARNING WE WERE QUITE SURPRISED
BY THE ACCURACY OF THE
PREDICTIONS THAT COULD BE MADE
BY THE METHOD THAT IS SIMPLY A
BLACK BOX.
YOU PUT THE GENOME IN AND
REGULATORY INFORMATION IN AND IT
STARTS MAKING ACCURATE
PREDICTIONS.
WE SAW IT TAKES TIME TO TRAIN
THE MODEL AND WE TRIED TO LOOK
AT DIFFERENT OPTIMIZERS AND WHAT
WE SAW, YOU CAN SEE IT FROM THE
ANIMATION ON THE RIGHT IS THAT
DIFFERENT OPTIMIZERS BEHAVE
QUITE DIFFERENTLY.
IT HAS BEEN TAKEN SO MUCH TIME
FOR US TO TRAIN -- FOR US TO
MODEL ON GOOGLE CLOUD AND
BIOWUFL AND THERE'S APPLICABLE
TO THE ENHANCERS AND WORKS QUITE
MAGICALLY AND QUITE FAST IN OUR
HANDS.
OKAY.
SO WE CAN PREDICT THE REGULATORY
ELEMENTS.
WHAT WE CAN DO, WE CAN PERFORM
IN SILICO MUTAGENESIS AND WE CAN
LOOK AT ANY SEQUENCE IN THE
HUMAN GENOME AND GET A SCORE OF
THE SEQUENCE OF HOW LIKELY IT IS
TO ACT AS AN ENHANCER AND START
CHANGING EVERY NUCLEOTIDE AND
SCORE IN EACH VARIANT CHANGE AND
SEE IF THERE IS ONE THAT WOULD
ENHANCE ACTIVITY.
WHAT YOU SEE IS WE CANNOT APPLY
EVERY SINGLE NUCLEOTIDE AND FOR
SOME OF THEM WE MIGHT GET
STRONGER EFFECT AND FOR SOME WE
MAY GET LOWER EFFECT.
WHAT I WOULD LIKE YOU TO KEEP IN
MIND IS POSITIVE CHANGES IS A
STRONGER NEGATIVE IMPART ON THE
ENHANCER ACTIVITY.
WHEN WE APPLY THE MODEL TO
BINDING OF TRANSCRIPTION
FACTORS, WHAT WE SAW IS THAT WE
CAN PREDICT BINDING OF ALLELE
TRANSCRIPTION FACTORS WITH
PRECISION.
SOMETHING THAT HAS NOT BEEN
POSSIBLE TO DO UNTIL THE
IMPLEMENTATION OF DEEP LEARNING.
THIS IS WHAT WE GET WHEN WE
PROFILE INDIVIDUAL ENHANCERS.
IT SUGGESTS RANDOM ENHANCER THAT
YOU SEE OVER HERE AND YOU SEE
PEAKS AND VALLEYS.
THESE ARE THE MEASURES OF THE
MUTATIONAL IMPACT ON THE
ACTIVITY OF THIS PARTICULAR
ENHANCER.
WHAT'S INTERESTING IS THAT WHEN
WE OVERLAY THAT WITH WHAT'S
KNOWN ABOUT BINDING OF SPECIFIC
TRANSCRIPTION FACTORS IT'S
ALMOST IDENTICALLY PREDICTS TWO
BINDING SITES OF THE HNF 4A AND
WE CAN RUN ANOTHER DEEP LEARNING
MODEL THAT ANALYZES THE ENHANCER
PROFILES AND CAN PREDICT TWO
ADDITIONAL TRANSCRIPTION
FACTORS.
WE PREDICT WHAT IS KNOWN AND
WHAT'S NEW.
THAT'S FINE.
HOW DOES IT SCORE?
SO WE DON'T CARE THAT MUCH ABOUT
PREDICTION OF ENHANCERS.
WE CARE ABOUT THE METHOD TO
PREDICT THIS MUTATION WITHIN THE
REGULATORY DNA.
THERE'S A HUGE EXPERIMENTAL DATA
SET, TENS OF THOUSANDS OF
VARIANTS THAT CHANGE CANCER
ACTIVITY IN THE HUMAN GENOME AND
WE COMPARED AND SAW THE DENSITY
OF THE VALIDATE THE PREDICTS IN
WHAT WE IDENTIFY IN BINDING
SITES OF THE ENHANCERS IN THE
FUNCTIONAL VARIANTS AND PREDICT
THE BINDING SITES SO MUCH CANCER
HOWEVER, WE ASKED THE QUESTION,
WHAT ABOUT THE SCORE OF OUR
METHOD?
IF WE DELINEATE EVERYTHING BY
THE SCORE AND SEE IF THE SCORE
OF THE PREDICTIONS LEADS WE SAW
INDEED THE DAMAGING EFFECT THE
GREATER THE EXPERIMENTAL SIGNAL
NOT ONLY THE METHOD TO PREDICT
BUT THE SCORING CAPABILITIES CAN
BE VALIDATED USING EXPERIMENTAL
DATA.
WE CAN PREDICT EFFECT OF
ENHANCER GENE BUT WHAT MAY BE
MORE INTERESTING IS WE CAN
PREDICT AND POSITIVE SCORING,
NEGATIVE ACTIVITY AND EFFECTS ON
THE ENHANCER ACTIVITY AND
SCORES, POSITIVE EFFECTS ON THE
ENHANCER ACTIVITY.
WE CAN PREDICT WHAT IS
SUBJECTIVE OF THE ENHANCER
ACTIVITY AND THAT WE'VE SEEN SO
OFTEN WE GET TONS AND IT HAS
BEEN CRAZY AND HAS BEEN
DIFFICULT UNTIL OUR COLLEAGUES
DEMONSTRATED OUR REGULATORY
AMOUNTS ARE OPEN TO INNOVATION
WHEN THEY TESTED ENHANCERS AND
THEY'VE DONE THIS IN MICE
THEY'VE BEEN ABLE TO SHOW THEY
DO NOT DESTROY ENHANCERS.
THEY STRENGTHEN OR CREATE NEW
ENHANCERS FROM THE PREVIOUS
ENHANCER TEMPLATES AND WE CAN
HAVE THE SAME EFFECT USING OUR
DEEP LEARNING MODEL.
WE JOINED WITH THE GENOME
INSTITUTE AND DECIDED TO LOOK AT
THE DISEASE CAUSAL MUTATIONS IN
TYPE II DIABETES.
THERE WAS A COLLECTION OF
ENHANCERS IN OUR METHOD AND
TRAIN THE MODEL AND START
PRED
PREDICTING NET ONLY ENHANCERS
BUT MUTATIONS LIKELY TO DISRUPT
THE ENHANCERS.
WE USED THAT DATA AND IT'S
LIKELY TO BE PREDICTED AS SUCH
FROM THE DATA.
I WOULD LIKE TO ASK YOU LOOK AT
THE PROFILERS OF ENHANCERS AND
WHAT YOU SEE, YOU SEE THE SAME
PEAKS AND VALLEYS AND YOU SEE
TWO SPECIFIC NARROW PEAKS WITHIN
THE CONSEQUENCE OF THE ENHANCER
AND DEPICTED BY THE
EXPERIMENTATION.
THERE ARE TWO NUCLEOTIDES IF
TOUCHED WILL DESTROY THE
ENHANCER.
IF YOU LOOK UNDER, THEY'RE OUR
COMPUTATIONAL PREDICTS AND ALIGN
WITH EXPERIMENTAL DATA AND HOW
METHOD PREDICT THE CAUSAL AND
DISRUPTIVE VARIANTS.
OKAY, WE CAN COMPARE OUR METHOD
TO OTHER METHODS AVAILABLE FOR
SIMILAR TASK AND WHAT WE'VE SEEN
ACROSS DIFFERENT CELL LINES AND
CONDITIONS OUR METHOD PERFORMS
AS GOOD OR OTHER METHODS OR
BETTER THAN WHAT'S AVAILABLE.
HOW CAN WE APPLY TO TYPE II
DIABETES TYPE II DIABETES?
AND IN PARTICULAR, HOW CAN WE
HELP THE COMMUNITY TO RESOLVE
THE UNCERTAINTY OF THE GWAS
STUDIES?
IF YOU START WITH DIABETES GWAS
THERE'S 400 SOME SNIPS AND THAT
WOULD BE FIND BUT YOU HAVE TO
HAVE THE STRUCTURE OF THE HUMAN
GENOME INTO THE SNIPS AND GOOD
LUCK TRYING TO FIGURE OUT WHICH
ONES ARE NOT SIMPLE.
AND LOOK AT THE SNIPS TO 50 AND
WE CAN MAKE TYPE II DIABETES
MORE FEASIBLE.
AND IN THE CASE OF RNF6 GENE
WHAT IS KNOWN IS UP STREAM OF
THE GENE THERE'S A LD BLOCK THAT
ALL LOOK IDENTICAL AND THEY ARE
LOOK ASSOCIATED WITH THE DISEASE
AS THEIR NEIGHBORS.
HOW CAN YOU LOOK AT THIS
DUPLICATE AND FIGURE OUT WHAT IS
THE CAUSE OF THE DISEASES?
WE APPLIED OUR METHOD ONLY ONCE
TO THAT AND LUCKILY THIS SNIP
HAS BEEN EXPERIMENTALLY
DEMONSTRATED TO BE THE CAUSAL
VARIANT OF THIS AND IT'S NOT
THAT INTERESTING HOWEVER, WHAT
WE CAN DO THE GENES LEAD TO
DISEASES AND WHEN WE LOOKED AT
SOME THAT'S ONE OF THE EXAMPLES.
IF YOU LOOK AT GWAS YOU SEE TON.
THAT'S THE BOTTOM PAIR.
IF YOU LOOK AT THE GLUCOSE YOU
GET FEWER VARIANTS BUT YOU STILL
GET QUITE A FEW ONLY A DOZEN
ASSOCIATED WITH THE DISEASE.
HOWEVER, WHEN YOU APPLY THE
METHOD YOU ONLY GET TWO AND ONE
OF THEM SEEMS TO BE MUCH
STRONGER CANDIDATE THAN THE
OTHER ONE.
AND THIS IS THE WORK OF OUR
COLLEAGUE FROM THE JACKSON LAB
AND THEY ARE HAVE BEEN ABLE TO
LOOK AT OUR PREDICTIONS WHERE WE
SAID, OKAY, IT LOOKS LIKE THERE
ARE THREE STRONG ASSOCIATIONS
AND WE PREDICT THERE'S ONLY ONE
IN THE MIDDLE THAT'S THE DISEASE
CAUSAL OR PHENOTYPE CAUSAL
ALLELE AND THEY'VE BEEN ABLE TO
CONFIRM JUST THAT USING ASSAYS
AS WELL AS THE REPORTER AND
DEMONSTRATING THE CHANGE WE
PREDICT AS CAUSAL THERE'S A
DRASTIC EFFECT IN THE GENE
EXPRESSION.
SO THAT'S GREAT, THAT'S FINE.
FOR US WE WERE HAPPY ABOUT THAT
AND THE PAPER HAS BEEN PUBLISHED
LAST YEAR BUT THIS IS THE GROUP
THAT MOST LIKELY USES MUCH MORE
ADVANCED METHODS THAN CNS.
I HAVE BEEN TALKING ABOUT CNNs
AND WE ASK THE SAME QUESTION.
SO, IS IT REASONABLE TO ASSUME
THAT THIS AREA OF ADVANCED A.I.,
CNN STILL MAKES SENSE?
AND AT LEAST WE HAVE THE
TRANSFORMER MODELS, THEY SEEM TO
BE MAKING A BREAKTHROUGH AND WE
ASKED THE SAME QUESTION, CAN WE
TAKE THIS MORE ADVANCED MODELS
AND APPLY THEM TO GENES AND
THERE HAVE BEEN TWO AREAS OF
RESEARCH IN THE REGULATORY
GENOMICS OR GENOMICS FIELD THAT
HAVE EMERGED AND THEY'RE BASED
ON TRANSFORMER MODELS.
ONE IS THE INDUSTRY DEVELOPED
MODEL CALLED NUCLEOTIDE
TRANSFORMER AND IT HAS USED
DOZENS OF GPUs, VERY EXPENSIVE
TO TRAIN AND THE OTHER IS DNA
AND THERE'S MODEL MIX
TRANSFORMERS WITH CNNs.
WE TOOK QUITE A FEW OF THESE
MODELS.
WE TOOK VARIANTS OF THE MODELS
AND WHAT WE DECIDED TO DO, WE
DECIDED TO LOOK AT THE
EXPERIMENTAL DATA ON THE PART OF
REGULATORY VARIANTS AND COMPARE
THE PREDICTIVE ABILITY WITH
OTHER MODELS.
YOU SEE THEY'RE COLOR CODED AND
THEY CAN RESPOND TO HYBRID OR
TRANSFORMER MODELS IN BLUE AND
SAW SECONDLY, CNNs WORK SO MUCH
BETTER.
WE HAVE NO EXPLANATION TO THIS
BUT WE CAN USE CNNs.
I TOOLED TOLD YOU ABO -- I TOLDU
ABOUT TYPE II DIABETES AND WOULD
LIKE TO TALK TO YOU ABOUT OTHER
DISEASES.
CALLED OUR MODEL AND
RAMIFICATION OF IT IS LIMITED TO
A SPECIFIC DISEASE OR CELL LINE.
MAYBE PANCREATIC ISLETS ONLY OR
COULD IT BE WE CAN BE ABLE TO
PREDICT ACCURATELY DISEASE
CAUSAL VARIANTS FOR OTHER
DISEASES?
AND WE IDENTIFIED STUDIES
FOCUSSING ON ALZHEIMER'S,
PARKINSON'S, SCHIZOPHRENIA, ALS
WHERE THE INFORMATION ON DISEASE
CAUSAL VARIANTS IS AVAILABLE.
SO WE KNOW AT LEAST A FEW OF
THEM WE CAN TRY TO REPLICATE AND
MAKE ACCURATE PREDICTIONS OR NOT
A
AND ALL, EVERY CAUSAL SNIP
THAT'S A RED TRIANGLE IS
PREDICTED AS A HIGHLY SCORING
DAMAGING VARIANT OR ENHANCER
VARIANT ACCORDING TO OUR MODEL.
AND CONFIRM AS CAUSAL IS NOT AS
HIGHLY SCORING SUGGESTING WE NOT
ONLY PREDICT WHAT'S KNOWN BUT
FROM VARIANTS THAT ARE LIKELY TO
BE VARIANTS OF DIFFERENT
DISEASES.
WITH THAT, I WOULD LIKE TO THANK
YOU FOR ORGANIZING THE EVENT AND
ANG LOOK AT SHUN LEE WHO HAS
DONE A LOT AND SHE'S NOW AN NCI
AND TO THANK OUR COLLABORATORS.
ONE THAT I'LL BE HAPPY IT ANSWER
QUESTIONS.
>> THANK YOU FOR THE SUPER
EXCITING PRESENTATION.
WE DO APPLICATIONS OF DEEP
LEARNING TO TEXT BUT TAKING THE
MODELS AND APPLYING TO BIOLOGY
AND PROTEINS AND DISEASES WHICH
IS A WHOLE NEW WORLD TO US WHICH
IS SO EXCITING.
I WANTED TO ASK YOU IF YOU COME
UP WITH NEW VARIANT PREDICTIONS
USING THIS DEEP LEARNING MODELS
DO YOU HAVE A MECHANISM TO
VERIFY AND HOW IS IT FOR US AT
NIH TO USE THE PREDICTIONS.
>> YOU'RE LOOKING AT THE HEART
OF THE PROBLEM.
VALIDATION IS KEY FOR US.
THERE'S EXPERIMENTAL DATA SETS
WE CAN USE TO VALIDATE OUR
MODEL.
WE CAN DO HIGH SCALE VALIDATIONS
BUT THEY'RE NOT TARGETED TO
SPECIFIC PREDICTIONS THAT WE
MAKE.
WOE HAVE WONDERFUL COLLABORATORS
AT THE NIH INCLUDING THOSE THAT
TAKE OUR PREDICTIONS AND LOOKS
AT REPORTER ASSAYS AND SHOW
COMPUTATION IN EXPERIMENTS AND
IT TAKES TIME AND MONEY.
AND I'M QUITE HAPPY TO BE PART
OF NIH WHERE WE HAVE A LARGE
NETWORK OF POSSIBLE
COLLABORATIONS.
AND SURE THE INTERESTED IN
ESTABLISHING BRIDGES BETWEEN
EXPERIMENTATION AND COMPUTATIONS
AND WHAT -- THANK YOU FOR THE
SCIENTIFIC ENVIRONMENT WHERE WE
HAVE PEOPLE AND GROUPS.
I HAVE ANOTHER QUESTION FROM THE
AUDIENCE AND HE'S ASKING DOES
THIS MODEL PREDICT WHETHER GENES
OR GENE EXPRESSION IS INFLUENCED
BY NON-CODING VARIANTS AND
DIRECTION OF EXPRESSION CHANGES
SUCH AS UPREGULATION OR DOWN
REGULATION.
>> IT'S A FANTASTIC QUESTION AND
THE ANSWER IS YES AND NO.
WE DO NOT PREDICT THE IMPACT OF
REGULATORY VARIANCE AND THERE'S
ANOTHER COMPLEX TASK AS I DIDN'T
GO IN THE DETAILS BUT YOU MIGHT
KNOW THAT EVERY GENE HAS A LARGE
NUMBER OF REGULATORY ENHANCERS.
THERE'S HALF A DOZEN ENHANCERS
YOU HAVE CHROMATIN CONFIRMATION
AND GENE REGULATION IS QUITE
COMPLEX.
WE PREDICT THE EFFECT ON OTHER
ELEMENTS.
AND FIGURING OUT THE ROLE OF
ENHANCERS AND HOW THEY
COLLABORATE AND UP AND DOWN
REGULATE THE ACTIVITY OF THE
GENES.
WE'RE TRYING TO VIEW THESE ALL
INCLUSIVE MODEL BUT SO FAR WE
HAVE NOT BEEN ABLE TO CROSS THAT
BRIDGE YET.
SO OUR MODEL PREDICTS THE IMPACT
ON THE ACTIVITY OF CANCERS AS A
DOWN STREAM EFFECT IT DOES
EFFECT GENE EXPRESSION BUT WE DO
NOT QUANTIFY THAT.
>> THANK YOU VERY MUCH.
>> THANK YOU SO MUCH.
I WANT TO IT'S 3:00.
I WE HAVE TO WRAP UP AND THANK
YOU FOR YOUR EXCITING IMPORTANT
WORK AND WE HOPE TO SEE YOU IN
THE FUTURE.
THANK YOU.